Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization.

Chemical diversity is one of the key term when dealing with machine learning and molecular generation. This is particularly true for quantum chemical datasets. The composition of which should be done meticulously since the calculation is highly time demanding.

EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation.

The objective of this work is to design a molecular generator capable of exploring known as well as unfamiliar areas of the chemical space. Our method must be flexible to adapt to very different problems. Therefore, it has to be able to work with or without the influence of prior data and knowledge.

MixONat, a Software for the Dereplication of Mixtures Based on C NMR Spectroscopy.

Whether chemists or biologists, researchers dealing with metabolomics require tools to decipher complex mixtures. As a part of metabolomics and initially dedicated to identifying bioactive natural products, dereplication aims at reducing the usual time-consuming process of known compounds isolation. Mass spectrometry and nuclear magnetic resonance are the most commonly reported analytical tools during dereplication analysis.

Dataset's chemical diversity limits the generalizability of machine learning predictions.

The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML molecular predictions have been recently published with an accuracy on par with Density Functional Theory calculations.